The influence of motion capture method, sex, and body mass index on lower extremity kinematics and intersession reliability during a drop-vertical jump

The purpose was to examine the effects of sex and body size on validity and intersession reliability of lower extremity kinematics from marker-less (ML) and marker-based (MB) during a drop vertical jump (DVJ). Forty participants were grouped by sex (male/female) and body size (high/low-BMI). Participants completed two sessions and lower extremity kinematics were recorded with 3D motion capture. Coefficient of multiple correlation (CMC) evaluated intersession reliability. A 2 (sex) by 2 (body size) by 2 (method) repeated measured ANOVA compared kinematic waveforms via statistical parametric mapping. In the sagittal plane, both methods showed very good to excellent reliability (CMC: 0.82-0.99). In the frontal and transverse planes, ML had moderate to very good reliability (CMC: 0.65-0.88), while MB had lower reliability that varied by group (CMC: 0.51-0.88). Discrepancies in sex-based comparisons occurred in the sagittal plane of the hip between methods, and body size comparisons in the frontal and transverse plane of the knee and ankle. Overall, ML had superior intersession reliability. However, motion capture method influences DVJ kinematics and may be due to differences in segment pose estimation between methods, and unique effects of body size and sex. Future studies should consider participant characteristics when implementing ML and interpreting findings.

Authors

Institutions

Publication Details

Journal
Journal of Sports Sciences
Published
2025-06-16
DOI
https://doi.org/10.1080/02640414.2025.2516309
Citations
1
Primary Topic
Lower Extremity Biomechanics and Pathologies
Type
article
Field-Weighted Citation Impact
0.67

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The influence of motion capture method, sex, and body mass index on lower extremity kinematics and intersession reliability during a drop-vertical jump

Derek N. Pamukoff, Iwi J. Eghobamien, Vital I. Nwaokoro, Neil A. Wills
1 citations
Journal of Sports Sciences
Lower Extremity Biomechanics and Pathologies
0.67
article

The influence of motion capture method, sex, and body mass index on lower extremity kinematics and intersession reliability during a drop-vertical jump

Derek N. Pamukoff, Iwi J. Eghobamien, Vital I. Nwaokoro, Neil A. Wills
article en
1 citations

Abstract

The purpose was to examine the effects of sex and body size on validity and intersession reliability of lower extremity kinematics from marker-less (ML) and marker-based (MB) during a drop vertical jump (DVJ). Forty participants were grouped by sex (male/female) and body size (high/low-BMI). Participants completed two sessions and lower extremity kinematics were recorded with 3D motion capture. Coefficient of multiple correlation (CMC) evaluated intersession reliability. A 2 (sex) by 2 (body size) by 2 (method) repeated measured ANOVA compared kinematic waveforms via statistical parametric mapping. In the sagittal plane, both methods showed very good to excellent reliability (CMC: 0.82-0.99). In the frontal and transverse planes, ML had moderate to very good reliability (CMC: 0.65-0.88), while MB had lower reliability that varied by group (CMC: 0.51-0.88). Discrepancies in sex-based comparisons occurred in the sagittal plane of the hip between methods, and body size comparisons in the frontal and transverse plane of the knee and ankle. Overall, ML had superior intersession reliability. However, motion capture method influences DVJ kinematics and may be due to differences in segment pose estimation between methods, and unique effects of body size and sex. Future studies should consider participant characteristics when implementing ML and interpreting findings.

Journal of Sports SciencesVol. 43(16)
Western University (CA)
Natural Sciences and Engineering Research Council of Canada
Gender equality
Openalex Percentile: Top 30%
Lower Extremity Biomechanics and Pathologies
0.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.